The vision of this program is to develop tumor forecasting methods by integrating quantitative imaging data and biophysical models of tumor growth to predict the response of individual tumors to therapy. Current mathematical models of tumor growth are limited in their practical applicability as they require input data that are extraordinarily difficult to obtain in an intact organism with any reasonable spatial resolution at even a single time point, let alone at multiple time points. Consequently, there has been very little application of such models to clinical data and virtually no incorporation into clinical trils. The motivation for integrating imaging data into mathematical models of tumor growth is that imaging can provide quantitative information noninvasively, in 3D, and at multiple time points. Measurements can be made (without disturbing the system) at the time of diagnosis and early in the course of treatment, and then these data can be modeled to predict response at the end of therapy. In this way, imaging allows models to be initialized with patient specific data. We believe we are now in the position to apply for support to perform a complete set of prospective studies appropriately designed for testing and validating two imaging based mathematical models of tumor growth and treatment response. To achieve this goal, we have identified the following two specific aims: 1. Determine the ability of dynamic contrast enhanced MRI and diffusion weighted MRI measurements of tumor vascular and cellular characteristics, respectively, obtained early in the course of therapy, to initialize the logistic model of tumor growth in order to predict final treatment response in individual animals. 2. Determine the abilit of MRI and PET measurements of tumor cellular, vascular, hypoxic, and glycolytic characteristics, obtained early in the course of therapy, to initialize a biophysical model of angiogenesis and cell growth in order to predict final treatment response in individual animals. Success in this line of investigation would allow for accurate prediction of treatment efficacy, so that ineffective therapies can be switched to potentially more effective approaches thereby enabling a practical, clinically relevant realization of personalized medicine for cancer patients.